Recognition or Disagreement

Recognition or Disagreement  eBooks & eLearning

Posted by Bayron at July 22, 2016
Recognition or Disagreement

Recognition or Disagreement: A Critical Encounter on the Politics of Freedom, Equality, and Identity by Jacques Rancière, Axel Honneth
English | 2016 | ISBN: 023117716X | 240 pages | EPUB | 2,2 MB
Recognition or Disagreement: A Critical Encounter on the Politics of Freedom, Equality, and Identity

Jacques Rancière, Axel Honneth, "Recognition or Disagreement: A Critical Encounter on the Politics of Freedom, Equality, and Identity"
ISBN: 023117716X | 2016 | EPUB | 240 pages | 2 MB
Recognition or Disagreement: A Critical Encounter on the Politics of Freedom, Equality, and Identity

Recognition or Disagreement: A Critical Encounter on the Politics of Freedom, Equality, and Identity (New Directions in Critical Theory) by Axel Honneth and Jacqu
English | 2016 | ISBN: 023117716X | 240 pages | PDF | 1,7 MB
"Pattern Recognition and Machine Learning" by Christopher M. Bishop

"Pattern Recognition and Machine Learning" by Christopher M. Bishop
Information Science and Statistics
Sрringеr Science+Business Media | 2006 | ISBN: 0387310738 9780387310732 | 761 pages | PDF | 5 MB

This textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Handbook of Face Recognition (Repost)  

Posted by lenami at Feb. 20, 2010
Handbook of Face Recognition  (Repost)

Handbook of Face Recognition
Publisher: Springer | ISBN: 038740595X | edition 2005 | PDF | 398 pages | 11,7 mb

Although the history of computer-aided face recognition stretches back to the 1960s, automatic face recognition remains an unsolved problem and still offers a great challenge to computer-vision and pattern recognition researchers. This handbook is a comprehensive account of face recognition research and technology, written by a group of leading international researchers. Twelve chapters cover all the sub-areas and major components for designing operational face recognition systems. Background, modern techniques, recent results, and challenges and future directions are considered. The book is aimed at practitioners and professionals planning to work in face recognition or wanting to become familiar with the state-of- the-art technology. A comprehensive handbook, by leading research authorities, on the concepts, methods, and algorithms for automated face detection and recognition. Essential reference resource for researchers and professionals in biometric security, computer vision, and video image analysis.

Pattern Recognition and Machine Learning (Repost)  eBooks & eLearning

Posted by foosaa at July 22, 2009
Pattern Recognition and Machine Learning (Repost)

Christopher M. Bishop, "Pattern Recognition and Machine Learning"
Springer | 2007 | ISBN: 0387310738 | English | 738 Pages | PDF | 9.5 MB

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Chris Satchwell - Pattern Recognition and Trading Decisions [Repost]  eBooks & eLearning

Posted by rotten comics at Nov. 30, 2016
Chris Satchwell - Pattern Recognition and Trading Decisions [Repost]

Chris Satchwell - Pattern Recognition and Trading Decisions
2004 | ISBN: 0071434801 | English | 370 pages | PDF | 2.2 MB

Hybrid Methods in Pattern Recognition [Repost]  eBooks & eLearning

Posted by tanas.olesya at Nov. 21, 2016
Hybrid Methods in Pattern Recognition [Repost]

Hybrid Methods in Pattern Recognition by Abraham Kandel
English | 22 May 2002 | ISBN: 9810248326 | 336 Pages | PDF | 15 MB

Hybrid Methods in Pattern Recognition is a collection of articles describing recent progress in this emerging field.

Managing Concussions in Schools : A Guide to Recognition, Response, and Leadership  eBooks & eLearning

Posted by readerXXI at Nov. 13, 2016
Managing Concussions in Schools : A Guide to Recognition, Response, and Leadership

Managing Concussions in Schools : A Guide to Recognition, Response, and Leadership
by Susan Davies
English | 2016 | ISBN: 0826169228 | 169 Pages | True PDF | 2.75 MB

This is the first comprehensive text for school staff, including psychologists, counselors, and nurses, on managing concussions in students, from prevention to post-concussion return to school.

Pattern Recognition  eBooks & eLearning

Posted by tanas.olesya at Oct. 13, 2016
Pattern Recognition

Pattern Recognition by Sergios Theodoridis Dr.
English | 27 Nov. 2008 | ISBN: 1597492728 | 957 Pages | PDF | 11 MB

This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering.